Date fruit grading and inspection is a challenging and crucial process in the industry. The grading process requires skilled and experienced labour. Moreover, the labour turnover in the date processing industries has been increased regularly. Therefore, due to the lack of trained labour, the quality of date fruit is often compromised. It leads to fruit wastage and instability of fruit prices. Currently, deep learning algorithms have achieved the research community's attention in solving the problems in the agriculture sector. The pre-trained models like VGG16 and VGG19 have been applied for the classification of date fruit [1,2]. Furthermore, machine learning techniques like K-Nearest Neighbors, Support Vector Machine, Random Forest and a few others [3], [4], [5], [6] have been used for grading of date fruit. Therefore, classification and sorting of date fruit problems have become common in the industry. The classification and grading of date fruit needed a neat and clean dataset. In this article, an indigenous and state-of-the-art dataset of date fruit is offered. The dataset contains images of four date fruit varieties. It consists of 3004 pre-processed images of different classes and grades. Moreover, images have been sorted based on size as large, medium, and small. Additionally, it is graded based on the quality as grade 1, grade 2, and grade 3. This dataset is separated into eighteen different directories for the facilitation of the researchers. It may contribute to develop an intelligent system to grade and inspect date fruit. This system may add value to the sustainable economic growth of fruit processing industries and farmers locally and internationally.
Keywords: Agriculture science; Artificial Intelligence; Classification; Deep learning; Machine learning.
© 2024 The Authors. Published by Elsevier Inc.